def plot_gc(gcstars, gcdata, gcID, cut=5.0, err=0.5,
modelfn='../data/PScolors.dat', DM=None, Ar=None):
d = gcstars[gcstars['gcID'] == gcID]
gc = gcdata[gcdata['ID'] == gcID]
# Filter out stars which are too far from cluster center
gc_rad = gc['r_h'] / 60.
if np.isnan(gc_rad):
gc_rad = gc['r_c'] / 60.
tp_star = np.array([(90. - d['b'])*np.pi/180., d['l']*np.pi/180.]).T
tp_gc = np.array([(90. - gc['b'])*np.pi/180., gc['l']*np.pi/180.]).T
gc_dist = great_circle_dist(tp_gc, tp_star)[0]
#idx = np.argsort(gc_dist)
#d = d[idx[:int(cut*len(idx))]]
idx = (gc_dist < cut * gc_rad * np.pi / 180.)
d = d[idx]
print 'Filtered out %.1f pct of stars based on distance.' % (100.*(1. - float(np.sum(idx))/float(len(idx))))
# Determine distance modulus to cluster
mu = 5. * (2. + np.log10(gc['R_Sun'][0]))
print 'Cluster ID: %s' % gcID
print 'Radius = %.2f deg' % gc_rad
print '# of stars = %d' % len(d)
print '(l, b): %.2f, %.2f' % (gc['l'], gc['b'])
print 'DM = %.2f' % mu
print '[Fe/H] = %.2f' % gc['FeH']
print 'E(B-V) = %.2f' % gc['EBV']
print 'A_r = %.2f' % (gc['EBV'] * 2.271)
# Correct observed magnitudes for extinction
A_coeff = np.array([3.172, 2.271, 1.682, 1.322, 1.087])
EBV = gc['EBV'] + 0.15
if DM != None:
mu = DM
if Ar != None:
EBV = Ar / A_coeff[1]
m_g = d['g'] - A_coeff[0] * EBV
m_r = d['r'] - A_coeff[1] * EBV
m_i = d['i'] - A_coeff[2] * EBV
m_z = d['z'] - A_coeff[3] * EBV
m_y = d['y'] - A_coeff[4] * EBV
# Determine model colors
model = StellarModel(modelfn)
idx = (d['g'] > 0.) & (d['r'] > 0.)
idx = idx & (d['g_err'] < 10.*err) & (d['r_err'] < 10.*err)
Mr = np.linspace(max(np.min(m_r[idx] - mu), -1.), min(np.max(m_r[idx] - mu), 27.), 200)
FeH = np.empty(200, dtype=np.float32)
FeH.fill(gc['FeH'])
model_color = []
for i in range(3):
model_color.append(model.color(Mr, FeH + 1.*(i-1.)))
mplib.rc('text', usetex=True)
'''
# Color - Color
fig = plt.figure(figsize=(8,8), dpi=100)
# g-r vs r-i
ax = fig.add_subplot(2,2,2)
idx = (d['g'] > 0.) & (d['r'] > 0.) & (d['i'] > 0.)
idx = idx & (d['g_err'] < err) & (d['r_err'] < err) & (d['i_err'] < err)
if np.any(idx):
density_scatter(ax, m_g[idx] - m_r[idx], m_r[idx] - m_i[idx],
binsize=(0.05,0.05), threshold=8)
ax.set_xlabel(r'$g - r$', fontsize=16)
ax.set_ylabel(r'$r - i$', fontsize=16)
for c,mc in zip(['r','g','c'], model_color):
ax.plot(mc['gr'], mc['ri'], c, alpha=0.5)
# r-i vs i-z
ax = fig.add_subplot(2,2,3)
idx = (d['r'] > 0.) & (d['i'] > 0.) & (d['z'] > 0.)
idx = idx & (d['r_err'] < err) & (d['i_err'] < err) & (d['z_err'] < err)
if np.any(idx):
density_scatter(ax, m_r[idx] - m_i[idx], m_i[idx] - m_z[idx],
binsize=(0.05,0.05), threshold=8)
ax.set_xlabel(r'$r - i', fontsize=16)
ax.set_ylabel(r'$i - z$', fontsize=16)
for c,mc in zip(['r','g','c'], model_color):
ax.plot(mc['ri'], mc['iz'], c, alpha=0.5)
# i-z vs z-y
ax = fig.add_subplot(2,2,4)
#for i in range(50):
# print d['i'][5*i:5*(i+1)]
# print d['i_err'][5*i:5*(i+1)]
# print ''
idx = (d['i'] > 0.) & (d['z'] > 0.) & (d['y'] > 0.)
idx = idx & (d['i_err'] < err) & (d['z_err'] < err) & (d['y_err'] < err)
if np.any(idx):
density_scatter(ax, m_i[idx] - m_z[idx], m_z[idx] - m_y[idx],
binsize=(0.05,0.05), threshold=8)
ax.set_xlabel(r'$i - z$', fontsize=16)
ax.set_ylabel(r'$z - y$', fontsize=16)
for c,mc in zip(['r','g','c'], model_color):
ax.plot(mc['iz'], mc['zy'], c, alpha=0.5)
'''
# Color - Magnitude
fig = plt.figure(figsize=(8,8), dpi=100)
# g-r vs M_r
ax = fig.add_subplot(2,2,1)
idx = (m_g > 0.) & (m_r > 0.)
idx = idx & (d['g_err'] < err) & (d['r_err'] < err)
if np.any(idx):
density_scatter(ax, m_g[idx] - m_r[idx], m_r[idx] - mu,
nbins=(500,500), threshold=100)
ax.set_xlim(-0.5, 0.9)
ylim = ax.get_ylim()
ax.set_ylim(ylim[1], ylim[0])
ax.set_xlabel(r'$g - r$', fontsize=16)
ax.set_ylabel(r'$M_{r}$', fontsize=16)
for c,mc in zip(['r','g','c'], model_color):
ax.plot(mc['gr'], Mr, c, alpha=0.5)
# r-i vs M_r
ax = fig.add_subplot(2,2,2)
idx = (m_r > 0.) & (m_i > 0.)
idx = idx & (d['r_err'] < err) & (d['i_err'] < err)
if np.any(idx):
density_scatter(ax, m_r[idx] - m_i[idx], m_r[idx] - mu,
nbins=(500,500), threshold=100)
ax.set_xlim(-0.5, 0.7)
ylim = ax.get_ylim()
ax.set_ylim(ylim[1], ylim[0])
ax.set_xlabel(r'$r - i$', fontsize=16)
ax.set_ylabel(r'$M_{r}$', fontsize=16)
for c,mc in zip(['r','g','c'], model_color):
ax.plot(mc['ri'], Mr, c, alpha=0.5)
# i-z vs M_r
ax = fig.add_subplot(2,2,3)
idx = (m_r > 0.) & (m_i > 0.) & (m_z > 0.)
idx = idx & (d['i_err'] < err) & (d['z_err'] < err)
if np.any(idx):
density_scatter(ax, m_i[idx] - m_z[idx], m_r[idx] - mu,
nbins=(500,500), threshold=100)
ax.set_xlim(-0.5, 0.6)
ylim = ax.get_ylim()
ax.set_ylim(ylim[1], ylim[0])
ax.set_xlabel(r'$i - z$', fontsize=16)
ax.set_ylabel(r'$M_{r}$', fontsize=16)
for c,mc in zip(['r','g','c'], model_color):
ax.plot(mc['iz'], Mr, c, alpha=0.5)
# z-y vs M_r
ax = fig.add_subplot(2,2,4)
idx = (m_r > 0.) & (m_z > 0.) & (m_y > 0.)
idx = idx & (d['z_err'] < err) & (d['y_err'] < err)
if np.any(idx):
density_scatter(ax, m_z[idx] - m_y[idx], m_r[idx] - mu,
nbins=(500,500), threshold=100)
ax.set_xlim(-0.5, 0.6)
ylim = ax.get_ylim()
ax.set_ylim(ylim[1], ylim[0])
ax.set_xlabel(r'$z - y$', fontsize=16)
ax.set_ylabel(r'$M_{r}$', fontsize=16)
for c,mc in zip(['r','g','c'], model_color):
ax.plot(mc['zy'], Mr, c, alpha=0.5)
# Source positions
fig = plt.figure(figsize=(8,8), dpi=100)
ax = fig.add_subplot(1,1,1)
density_scatter(ax, d['l'], d['b'], nbins=(100,100), threshold=20)
ax.set_xlabel(r'$\ell$', fontsize=16)
ax.set_ylabel(r'$b$', fontsize=16)
def plot_colors(d, model): # Correct for reddening A_coeff = np.array([3.634, 2.241, 1.568, 1.258, 1.074]) m_g = d['g'] - A_coeff[0] * d['EBV'] m_r = d['r'] - A_coeff[1] * d['EBV'] m_i = d['i'] - A_coeff[2] * d['EBV'] m_z = d['z'] - A_coeff[3] * d['EBV'] m_y = d['y'] - A_coeff[4] * d['EBV'] sel = (np.abs((m_g - m_r) - 0.715) < 10.02) & (np.abs((m_r - m_i) - 0.318) < 10.02) fig = plt.figure(figsize=(12,8), dpi=150) err = 0.02 # Color corrections (by eye) iz_corr = Delta_iz() model['iz'] += iz_corr(model['ri']) zy_corr = Delta_zy() model['zy'] += zy_corr(model['ri']) # Metallicity masks msel = np.abs(model['Mr'] + 0.5) < 100.01 rich = msel & (model['FeH'] == 0.) poor = msel & (model['FeH'] == -2.5) # g-r vs r-i ax = fig.add_subplot(2,3,1) idx = sel & (m_g > 0.) & (m_r > 0.) & (m_i > 0.) idx = idx & (d['g_err'] < err) & (d['r_err'] < err) & (d['i_err'] < err) gr = m_g[idx] - m_r[idx] ri = m_r[idx] - m_i[idx] idx = (gr > - 0.5) & (gr < 2.) & (ri > -0.5) & (ri < 2.) density_scatter(ax, gr[idx], ri[idx], nbins=(80,80), threshold=3, s=1.5) ax.plot(model['gr'][rich], model['ri'][rich], 'k.', ms=0.3) #ax.plot(model['gr'][poor], model['ri'][poor], 'g.', ms=0.2) ax.set_xlabel(r'$g - r$', fontsize=16) ax.set_ylabel(r'$r - i$', fontsize=16) ax.set_xlim(-0.5, 2.) ax.set_ylim(-0.5, 2.) # r-i vs i-z ax = fig.add_subplot(2,3,2) idx = sel & (m_r > 0.) & (m_i > 0.) & (m_z > 0.) idx = idx & (d['r_err'] < err) & (d['i_err'] < err) & (d['z_err'] < err) ri = m_r[idx] - m_i[idx] iz = m_i[idx] - m_z[idx] idx = (ri > -0.5) & (ri < 2.) & (iz > -0.25) & (iz < 1.) density_scatter(ax, ri[idx], iz[idx], nbins=(80,80), threshold=3, s=1.5) ax.plot(model['ri'][rich], model['iz'][rich], 'k.', ms=0.3) #ax.plot(model['ri'][poor], model['iz'][poor]+0.030, 'g.', ms=0.2) ax.set_xlabel(r'$r - i', fontsize=16) ax.set_ylabel(r'$i - z$', fontsize=16) ax.set_xlim(-0.5, 2.) ax.set_ylim(-0.25, 1.) # i-z vs z-y ax = fig.add_subplot(2,3,3) idx = sel & (m_i > 0.) & (m_z > 0.) & (m_y > 0.) idx = idx & (d['i_err'] < err) & (d['z_err'] < err) & (d['y_err'] < err) iz = m_i[idx] - m_z[idx] zy = m_z[idx] - m_y[idx] idx = (iz > -0.25) & (iz < 1.) & (zy > -0.2) & (zy < 0.6) density_scatter(ax, iz[idx], zy[idx], nbins=(80,80), threshold=3, s=1.5) ax.plot(model['iz'][rich], model['zy'][rich], 'k.', ms=0.3) #ax.plot(model['iz'][poor]+0.030, model['zy'][poor]-0.035, 'g.', ms=0.2) ax.set_xlabel(r'$i - z$', fontsize=16) ax.set_ylabel(r'$z - y$', fontsize=16) ax.set_xlim(-0.25, 1.) ax.set_ylim(-0.2, 0.6) # g-r vs z-y ax = fig.add_subplot(2,3,4) idx = sel & (m_g > 0.) & (m_r > 0.) & (m_z > 0.) & (m_y > 0.) idx = idx & (d['g_err'] < err) & (d['r_err'] < err) & (d['z_err'] < err) & (d['y_err'] < err) gr = m_g[idx] - m_r[idx] zy = m_z[idx] - m_y[idx] idx = (gr > - 0.5) & (gr < 2.) & (zy > -0.2) & (zy < 0.6) density_scatter(ax, gr[idx], zy[idx], nbins=(80,80), threshold=3, s=1.5) ax.plot(model['gr'][rich], model['zy'][rich], 'k.', ms=0.3) ax.set_xlabel(r'$g - r$', fontsize=16) ax.set_ylabel(r'$z - y$', fontsize=16) ax.set_xlim(-0.5, 2.) ax.set_ylim(-0.2, 0.6) # g-r vs i-z ax = fig.add_subplot(2,3,5) idx = sel & (m_g > 0.) & (m_r > 0.) & (m_i > 0.) & (m_z > 0.) idx = idx & (d['g_err'] < err) & (d['r_err'] < err) & (d['i_err'] < err) & (d['z_err'] < err) gr = m_g[idx] - m_r[idx] iz = m_i[idx] - m_z[idx] idx = (gr > - 0.5) & (gr < 2.) & (iz > -0.25) & (iz < 1.) density_scatter(ax, gr[idx], iz[idx], nbins=(80,80), threshold=3, s=1.5) ax.plot(model['gr'][rich], model['iz'][rich], 'k.', ms=0.3) ax.set_xlabel(r'$g - r$', fontsize=16) ax.set_ylabel(r'$i - z$', fontsize=16) ax.set_xlim(-0.5, 2.) ax.set_ylim(-0.25, 1.) # r-i vs z-y ax = fig.add_subplot(2,3,6) idx = sel & (m_g > 0.) & (m_r > 0.) & (m_z > 0.) & (m_y > 0.) idx = idx & (d['r_err'] < err) & (d['i_err'] < err) & (d['z_err'] < err) & (d['y_err'] < err) ri = m_r[idx] - m_i[idx] zy = m_z[idx] - m_y[idx] idx = (ri > - 0.5) & (ri < 2.) & (zy > -0.2) & (zy < 0.6) density_scatter(ax, ri[idx], zy[idx], nbins=(80,80), threshold=3, s=1.5) ax.plot(model['ri'][rich], model['zy'][rich], 'k.', ms=0.3) ax.set_xlabel(r'$r - i$', fontsize=16) ax.set_ylabel(r'$z - y$', fontsize=16) ax.set_xlim(-0.5, 2.) ax.set_ylim(-0.2, 0.6) fig.subplots_adjust(left=0.08, right=0.95, bottom=0.1, top=0.9, wspace=0.3)